Hands and Face Segmentation with Deep Convolutional Networks using limited labeled data.
Training.
from models import model1, model2
from keras import Model
import metrics
from keras.applications.vgg16 import VGG16
modelSNet = model1.SNet() # For using unet based architecture
# Using vgg based model.
modelVgg = VGG16(weights="imagenet", include_top=False, input_shape=(400,400,3))
base_model = Model(inputs=model.layers[0].output, outputs=model.layers[10].output)
# Freeze vgg layers.
for layer in base_model.layers:
layer.trainable = False
modelVGG = model2.vggPre(base_model)
modelVGG.fit(...)
modelSNet.fit(...)
# Metrics for evaluting models, if you want to use pretrained model.
dependencies = {
'f1_m' = metrics.f1_m,
'recall_m' = metrics.recall_m,
'precision_m' = metrics.precision_m
}
Ankara University Computer Vision & Machine Learning Labaratory (CVML LAB) Turkish Sign Language (TSL) Dataset.
For this study we used only a small portion ( 400 frames ) of the dataset which is consist of Turkish Sign Language videos (228 words ×∼ 150 samples ≈ 34.200 sample videos).You can access numpy version here:
- Images: https://drive.google.com/file/d/10zZun4ruE8jQZFvX5Ec6TS7OFmwQW6K8/view?usp=sharing
- Labels: https://drive.google.com/file/d/1WAnuNv_mFntQ2yrz1l7lzddU5VP2TQOl/view?usp=sharing
( The whole dataset will be published soon at https://cvml.ankara.edu.tr/ )
Model-1
Model-2
Model-1's performance is better than pretrained-vgg model ( model-2).
Note: Our research has been accepted by IEEE 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies with paper title " Hand and Face Segmentation with Deep Convolutional Networks using Limited Labelled Data". eexplore.ieee.org/document/8932835.